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00M-639 exam Dumps Source : IBM gargantuan Data Sales Mastery Test v1

Test Code : 00M-639
Test appellation : IBM gargantuan Data Sales Mastery Test v1
Vendor appellation : IBM
: 51 actual Questions

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IBM IBM gargantuan Data Sales

$18.seventy seven Billion in income anticipated for IBM (IBM) This Quarter | killexams.com actual Questions and Pass4sure dumps

Brokerages foretell that IBM (NYSE:IBM) will report $18.77 billion in earnings for the existing fiscal quarter, in response to Zacks. five analysts possess issued estimates for IBM’s profits, with estimates ranging from $18.forty three billion to $19.26 billion. IBM posted income of $19.07 billion within the very quarter final yr, which implies a negative year over 12 months boom cost of 1.6%. The company is scheduled to file its subsequent salary effects on Tuesday, April sixteenth.

in accordance with Zacks, analysts foretell that IBM will record full-year income of $78.31 billion for the current fiscal 12 months, with estimates ranging from $76.85 billion to $eighty.70 billion. For the next fiscal 12 months, analysts foretell that the enterprise will report sales of $78.09 billion, with estimates starting from $seventy seven.02 billion to $seventy nine.65 billion. Zacks’ revenue averages are an objective commonplace based on a survey of sell-facet analysts that cowl IBM.

IBM (NYSE:IBM) final posted its quarterly profits statistics on Tuesday, January 22nd. The know-how industry suggested $4.87 profits per share (EPS) for the quarter, beating the consensus assess of $four.eighty two through $0.05. The industry had income of $21.seventy six billion during the quarter, in comparison to analysts’ expectations of $21.seventy nine billion. IBM had a web margin of 10.ninety seven% and a revert on equity of 68.sixty one%. The business’s income became down three.5% on a yr-over-yr basis. throughout the very term final year, the solid posted $5.14 income per share.

IBM has been the subject of a few fresh analysis stories. Wedbush reduce their target expense on shares of IBM from $185.00 to $a hundred sixty five.00 and set a “impartial” rating for the company in a research note on Thursday, October 18th. Zacks investment research raised shares of IBM from a “promote” rating to a “cling” score in a research subsist awake on Thursday, October 18th. ValuEngine raised shares of IBM from a “promote” rating to a “hold” ranking in a research subsist awake on Wednesday. Goldman Sachs community restated a “neutral” ranking and issued a $155.00 cost goal on shares of IBM in a analysis record on Monday, October twenty ninth. eventually, BMO Capital Markets restated a “dangle” ranking and issued a $one hundred forty five.00 cost goal on shares of IBM in a analysis file on Friday, December seventh. Three funding analysts possess rated the inventory with a sell score, eleven possess issued a grasp ranking and eight possess issued a buy ranking to the company. IBM prerogative now has a consensus ranking of “dangle” and a consensus goal rate of $154.56.

IBM stock traded down $0.26 on Monday, hitting $137.27. 1,202,955 shares of the enterprise’s stock traded fingers, compared to its usual volume of 5,224,408. IBM has a 1-12 months low of $105.ninety four and a 1-year towering of $162.eleven. The enterprise has a market cap of $124.98 billion, a PE ratio of 9.94, a P/E/G ratio of 2.37 and a beta of 1.25. The enterprise has a debt-to-fairness ratio of 2.10, a present ratio of 1.29 and a brief ratio of 1.24.

The enterprise additionally lately declared a quarterly dividend, which may subsist paid on Saturday, March 9th. investors of checklist on Friday, February 8th should subsist given a $1.57 dividend. The ex-dividend date of this dividend is Thursday, February seventh. This represents a $6.28 annualized dividend and a dividend yield of 4.58%. IBM’s dividend payout ratio (DPR) is prerogative now forty five.47%.

IBM introduced that its Board of directors has accepted a inventory buyback draw on Tuesday, October thirtieth that permits the company to repurchase $four.00 billion in shares. This repurchase authorization allows the expertise company to reacquire up to three.5% of its stock through open market purchases. inventory repurchase plans are often a demonstration that the enterprise’s board believes its shares are undervalued.

In other IBM news, insider Diane J. Gherson bought 5,754 shares of the company’s stock in a transaction that took locality on Wednesday, February 6th. The shares possess been bought at a standard fee of $135.67, for a total cost of $780,645.18. Following the transaction, the insider now owns 23,117 shares in the enterprise, valued at about $3,136,283.39. The transaction become disclosed in a doc filed with the SEC, which can subsist accessed through this hyperlink. 0.17% of the inventory is at the moment owned via company insiders.

Institutional traders possess these days added to or decreased their stakes in the enterprise. Cozad Asset management Inc. multiplied its stake in IBM by means of 39.2% in the 4th quarter. Cozad Asset administration Inc. now owns 3,171 shares of the expertise business’s stock valued at $360,000 after purchasing an additional 893 shares totality over the period. Albion fiscal community UT elevated its stake in IBM by 1.5% in the third quarter. Albion fiscal neighborhood UT now owns 18,471 shares of the technology business’s stock valued at $2,793,000 after buying an extra 281 shares prerogative through the length. Paloma companions administration Co improved its stake in IBM through 127.4% in the third quarter. Paloma companions administration Co now owns 1,453 shares of the expertise business’s stock valued at $220,000 after buying an additional 6,757 shares during the length. Crossvault Capital administration LLC elevated its stake in IBM by way of 12.four% within the third quarter. Crossvault Capital administration LLC now owns 7,seven-hundred shares of the technology enterprise’s inventory valued at $1,164,000 after purchasing an extra 850 shares prerogative through the length. at last, Edmp Inc. elevated its stake in IBM by using 2.3% within the 4th quarter. Edmp Inc. now owns eleven,032 shares of the technology business’s inventory valued at $1,254,000 after purchasing an extra 243 shares totality over the length. Hedge funds and different institutional investors personal 61.97% of the company’s inventory.

IBM industry Profile

overseas enterprise Machines agency operates as an integrated technology and features industry global. Its Cognitive options segment presents Watson, a computing platform that interacts in language, strategies gargantuan statistics, and learns from interactions with americans and computer systems. This section additionally presents records and analytics solutions, including analytics and statistics management platforms, cloud information services, industry social utility, aptitude management solutions, and tailored industry solutions; and transaction processing application that runs mission-essential systems in banking, airlines, and retail industries.

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IBM Db2 question Optimization the spend of AI | killexams.com actual Questions and Pass4sure dumps

In September 2018, IBM announced a brand original product, IBM Db2 AI for z/OS. This ersatz intelligence engine screens statistics entry patterns from executing SQL statements, uses computing device discovering algorithms to elect on most beneficial patterns and passes this suggestions to the Db2 query optimizer for spend by way of subsequent statements.

desktop getting to know on the IBM z Platform

In may of 2018, IBM announced version 1.2 of its computer researching for z/OS (MLz) product. here is a hybrid zServer and cloud application suite that ingests performance records, analyzes and builds models that symbolize the fitness popularity of numerous indicators, displays them over time and offers real-time scoring capabilities.

a brace of facets of this product offering are aimed toward helping a community of model developers and executives. as an instance:

  • It helps diverse programming languages equivalent to Python, Scala and R. This permits facts modelers and scientists to spend a language with which they are general;
  • A graphical person interface known as the visual model Builder guides model developers without requiring totally-technical programming abilities;
  • It contains numerous dashboards for monitoring model effects and scoring features, in addition to controlling the device configuration.
  • This machine getting to know suite turned into at the nascence aimed toward zServer-based analytics applications. some of the first evident choices changed into zSystem performance monitoring and tuning. paraphernalia management Facility (SMF) statistics that are immediately generated with the aid of the working gadget deliver the raw records for gadget resource consumption reminiscent of material processor utilization, I/O processing, reminiscence paging etc. IBM MLz can compile and store these facts over time, and build and train models of system behavior, rating these behaviors, determine patterns no longer without difficulty foreseen with the aid of humans, multiply key efficiency warning signs (KPIs) and then feed the model results returned into the gadget to possess an consequence on gadget configuration adjustments that can multiply performance.

    The subsequent step changed into to enforce this suite to investigate Db2 performance statistics. One solution, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the machine learning technology to Db2 operational records to capitalize an knowing of Db2 subsystem fitness. it can dynamically construct baselines for key performance warning signs, give a dashboard of those KPIs and give operational group of workers real-time insight into Db2 operations.

    while time-honored Db2 subsystem performance is a crucial aspect in ordinary utility fitness and performance, IBM estimates that the DBA aid staff spends 25% or more of its time, " ... fighting access direction issues which trigger efficiency degradation and repair influence.". (See Reference 1).

    AI comes to Db2

    trust the plight of concomitant DBAs in a Db2 environment. In modern-day IT world they should lead one or extra great statistics purposes, cloud software and database functions, software setting up and configuration, Db2 subsystem and application performance tuning, database definition and administration, catastrophe recovery planning, and more. question tuning has been in being considering the origins of the database, and DBAs are continually tasked with this as neatly.

    The heart of query route evaluation in Db2 is the Optimizer. It accepts SQL statements from purposes, verifies authority to access the facts, studies the locations of the objects to subsist accessed and develops a listing of candidate statistics access paths. These access paths can involve indexes, desk scans, quite a few desk subsist fraction of methods and others. within the information warehouse and massive facts environments there are always further selections accessible. One of those is the being of summary tables (on occasion called materialized question tables) that comprise pre-summarized or aggregated records, accordingly allowing Db2 to prevent re-aggregation processing. another alternative is the starjoin access path, general within the information warehouse, where the order of desk joins is modified for efficiency reasons.

    The Optimizer then stories the candidate entry paths and chooses the entry path, "with the bottom cost." saturate in this context skill a weighted summation of aid usage including CPU, I/O, reminiscence and different substances. finally, the Optimizer takes the bottom can saturate entry path, stores it in remembrance (and, optionally, within the Db2 directory) and starts off entry course execution.

    big records and statistics warehouse operations now consist of application suites that enable the enterprise analyst to spend a graphical interface to build and exploit a miniature data mannequin of the records they are looking to analyze. The packages then generate SQL statements in keeping with the clients’ requests.

    The problem for the DBA

    to subsist able to attain trustworthy analytics for your diverse facts stores you necessity an outstanding realizing of the facts requirements, an figuring out of the analytical services and algorithms attainable and a high-performance statistics infrastructure. sadly, the quantity and location of data sources is expanding (each in measurement and in geography), records sizes are turning out to be, and applications proceed to proliferate in quantity and complexity. How should still IT managers assist this ambiance, particularly with essentially the most skilled and mature team of workers nearing retirement?

    take into account additionally that a gargantuan fraction of decreasing the overall saturate of ownership of these programs is to gain Db2 functions to elope quicker and greater efficiently. This constantly translates into the usage of fewer CPU cycles, doing fewer I/Os and transporting much less information across the network. when you reckon that it is regularly intricate to even identify which functions might improvement from performance tuning, one strategy is to automate the detection and correction of tuning issues. here is where desktop researching and ersatz intelligence can besides subsist used to incredible effect.

    Db2 12 for z/OS and synthetic Intelligence

    Db2 version 12 on z/OS uses the computer researching amenities outlined above to collect and retain SQL query textual content and entry course details, as well as genuine performance-linked traditional assistance similar to CPU time used, elapsed instances and consequence set sizes. This providing, described as Db2 AI for z/OS, analyzes and retailers the data in computing device researching fashions, with the mannequin evaluation outcomes then being scored and made available to the Db2 Optimizer. The subsequent time a scored SQL statement is encountered, the Optimizer can then spend the mannequin scoring facts as input to its entry course option algorithm.

    The outcome may still subsist a reduction in CPU consumption as the Optimizer makes spend of model scoring input to select improved entry paths. This then lowers CPU costs and speeds application response instances. a gargantuan talents is that using AI application does not require the DBA to possess information science erudition or abysmal insights into query tuning methodologies. The Optimizer now chooses the most desirable entry paths primarily based not most effectual on SQL question syntax and records distribution information but on modelled and scored historical efficiency.

    This will besides subsist certainly vital if you deliver data in separate areas. for instance, many analytical queries in opposition t huge information require concurrent access to determined records warehouse tables. These tables are generally known as dimension tables, and that they involve the data facets usually used to manage subsetting and aggregation. as an instance, in a retail environment believe a table known as StoreLocation that enumerates every shop and its region code. Queries against retain earnings records may additionally are looking to combination or summarize earnings by vicinity; therefore, the StoreLocation table should subsist used via some gargantuan records queries. during this ambiance it is usual to engage the dimension tables and duplicate them continually to the gargantuan data software. within the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).

    Now suppose about SQL queries from each operational purposes, information warehouse users and gargantuan records company analysts. From Db2's point of view, totality these queries are equal, and are forwarded to the Optimizer. however, in the case of operational queries and warehouse queries they may still absolutely subsist directed to access the StoreLocation desk within the warehouse. even so, the query from the industry analyst towards gargantuan data tables should doubtless access the copy of the desk there. This results in a proliferations of advantage entry paths, and more work for the Optimizer. luckily, Db2 AI for z/OS can give the Optimizer the guidance it needs to bear smart access path choices.

    how it Works

    The sequence of events in Db2 AI for z/OS (See Reference 2) is generally the following:

  • all over a bind, rebind, prepare or account for operation, an SQL commentary is passed to the Optimizer;
  • The Optimizer chooses the data access route; as the election is made, Db2 AI captures the SQL syntax, entry course alternative and query performance data (CPU used, and so forth.) and passes it to a "learning assignment";
  • The researching assignment, which can subsist finished on a zIIP processor (a non-familiar-goal CPU core that does not factor into utility licensing costs), interfaces with the laptop getting to know software (MLz mannequin functions) to retain this information in a mannequin;
  • because the volume of statistics in every model grows, the MLz Scoring service (which can besides subsist achieved on a zIIP processor) analyzes the mannequin statistics and scores the habits;
  • all over the next bind, rebind, locality together or explain, the Optimizer now has entry to the scoring for SQL models, and makes applicable adjustments to entry route decisions.
  • There are besides numerous consumer interfaces that give the administrator visibility to the repute of the collected SQL statement performance statistics and mannequin scoring.

    summary

    IBM's laptop getting to know for zOS (MLz) offering is getting used to exquisite consequence in Db2 edition 12 to multiply the efficiency of analytical queries as well as operational queries and their associated purposes. This requires management attention, as you possess to determine that your industry is prepared to consume these ML and AI conclusions. How will you measure the prices and advantages of the spend of machine researching? Which IT aid staff ought to subsist tasked to reviewing the consequence of mannequin scoring, and perhaps approving (or overriding) the consequences? How will you evaluation and justify the assumptions that the utility makes about access direction decisions?

    In different phrases, how well were you awake your statistics, its distribution, its integrity and your existing and proposed entry paths? this can determine the locality the DBAs spend their time in aiding analytics and operational utility efficiency.

    # # #

    Reference 1

    John Campbell, IBM Db2 unique EngineerFrom "IBM Db2 AI for z/OS: multiply IBM Db2 software efficiency with machine researching"https://www.worldofdb2.com/activities/ibm-db2-ai-for-z-os-boost-ibm-db2-utility-performance-with-ma

    Reference 2

    Db2 AI for z/OShttps://www.ibm.com/aid/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html

    See totality articles via Lockwood Lyon


    Why IBM is having a stake massive on this original huge records know-how | killexams.com actual Questions and Pass4sure dumps

    IBM plans an even bigger propel into records crunching through opening a brand original technology middle in San Francisco committed to a trendy know-how that’s making waves in Silicon Valley, Bloomberg information experiences.

    Rob Thomas, an IBM (IBM) vice chairman in can saturate of huge records, pointed out in a web video seen with the aid of Bloomberg and later eliminated that the brand original headquarters will at final condominium “hundreds of americans” working basically with a free expertise called Spark.

    Spark lets companies mode statistics more immediately than what is at the moment feasible the usage of an additional open-supply technology known as Hadoop, according to many analysts. among other things, groups spend Spark for quickly evaluation of sales facts relish what number of department deliver customers purchased a particular shirt.

    The expertise can work with or change Hadoop, which has won traction in recent years with agencies relish Yahoo (YHOO) and facebook (FB) that spend it to shop and mode massive amounts of records. relish with a lot of know-how, what’s peppery in statistics crunching alterations quickly as original utility emerges it truly is faster and simpler to use.

    It’s as a result of this velocity and skill to manner information to rapidly that has IBM excited. The a hundred-yr historic industry has been public with its assist for the technology and has claimed that it will besides subsist used to boost the performance of Hadoop.

    IBM has made information evaluation a gargantuan a fraction of its earnings pitch, fraction of which revolves around Watson, the robot that made an appearance on the Jeopardy tv video game demonstrate. In April, the enterprise launched its Watson health service that corporations can spend to dissect healthcare facts.

    It’s dubious what IBM plans for Spark. however it may support with making the underlying technologies behind Watson or equivalent features forward to lifestyles.

    by way of helping Spark and attracting employees who know the way to spend the infrastructure technology, IBM can declare that it’s ahead of the pack in reducing-area technology.

    With its hardware earnings generating less profits than it they once did, IBM increasingly relying on original know-how to revitalize its business. huge information technology may well subsist a much bigger a fraction of the plan.

    For extra on IBM and great information, check out here Fortune video:


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    IBM gargantuan Data Sales Mastery Test v1

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    From Bootcamp to Mastery: A Five Year Journey | killexams.com actual questions and Pass4sure dumps

    As I search for across the learn-to-code industry — with the proliferation of bootcamps, MOOCs, and alternative learning options — I often wonder why they (Launch School) are the only program that’s 100% mastery-based. There aren’t a lot of viable pedagogical options from which to choose, especially if the focus is on skills and results rather than credentialism. Yet, no one teaches in a mastery-based way except us. As I thought more about this, I realized that we, too, started teaching programming in a typical “bootcamp” fashion, and it was due to a unique confluence of personal and industry factors that led us to focus on mastery-based learning.

    This is a sage about how they built Launch School over the final 5 years and how their opinions around programming, teaching, and industry led us to a Mastery-based pedagogy.

    The Backstory

    I’ve known Kevin since 2002, when they were both software engineers at IBM. They had always talked about working on something together, but the occasion never came up. Finally around 2012, they had a window of time where both of us were looking to attain something new. They only knew that they wanted to work on something together, but didn’t possess any concrete ideas. After months of careful deliberation, they decided to focus their thirties on Education.

    Of course as programmers, the first thing they set out to attain was to build a revolutionary Learning Management System (LMS) that would halt totality LMSes. As they worked through the specifications and design, one thing became painfully obvious: they had no credence what they were doing because neither of us had any abysmal suffer with teaching or education. So naturally, before they could build a LMS, they had to gain some suffer teaching actual students. Now, I’d relish to reflect that we’re both pretty well-rounded people with a lot of interests, but they both really only had one skill that could attract students: programming. Towards the halt of 2012, they decided to give up their (extremely) towering paying jobs and try teaching people programming so they could better understand the problems around education and teaching (…so they could build an LMS to halt totality LMSes).

    I share this backstory because this origin sage will forward back to influence many of their later decisions. It’s critical to remember: they didn’t descry an occasion to bear money and came into teaching programming as an exercise in learning about how to educate people.

    Side Note: they quickly dropped the LMS credence because they institute out students don’t buy LMSes, and selling a original LMS to great organizations requires a skill that they weren’t interested in developing.

    2012–2013: Bootcamps

    Unbeknownst to us at the time, this was the golden era of learning to code. In an odd case of multiple-discovery, they started their teach-people-to-code exploration at nearly the very time as many other companies, who later collectively came to subsist known as “coding bootcamps”. It was during this term that a few intrepid companies were starting to prove that you could gain graduates a towering paying salary after training for only a few months. That short duration caught everyone’s eye. Dev Bootcamp, in particular, nearly single-handedly created the “coding bootcamp” industry; to this day, it’s called “coding bootcamps” mostly because of Dev Bootcamp.

    I happened to subsist based out San Francisco at the time and met with Shereef Bishay, founder of Dev Bootcamp, in their Chinatown office. Shereef became interested in what Kevin and I were doing and offered a partnership: they could roll their courses under the Dev Bootcamp brand and become their “preparatory” program. Because of their initial success, Dev Bootcamp started attracting a larger variety of students and many of their applicants lacked readiness. Not being interested in working for someone else, they declined. Besides meeting Shereef, I besides grabbed beers with other local bootcamp founders, relish Roshan Choxi and Dave Paola, founders of Bloc.io. It felt relish something gargantuan was about to chance in the industry and San Francisco was the epicenter.

    Meanwhile, Kevin and I continued executing their cohort-based courses. Their courses during this term were similar to ones you’d find in college: daily live lectures with a cohort of about 20–30 students with courses that lasted about a month. I had recently attended an online GMAT prep course offered by Knewton (they no longer attain this) and was inspired by the format of their live lectures combined with ad-hoc quizzes. It forced participants to pay attention and ensue along, and it felt relish a much better suffer than a typical college lecture, where you could sulk in the back of a great classroom and not ever engage with the instructor. The credence seemed promising: using innovative online tools, they could teach small live cohorts and ensure that everyone engaged with the material.

    In order to design out what topics to teach, they asked students what they would subsist interested in learning. Not surprisingly, they mentioned totality the advanced topics that employers demanded: TDD, APIs, Rails and Angular (this was before React was popular), testing, algorithms, data structures, design patterns, best practices, etc. By this point, Kevin and I each had over 10 years of software engineering experience, so the list of topics seemed straight-forward enough and they set out to teach them.

    The problems they encountered were immediate and obvious.

  • Student readiness levels elope the gamut. It’s impossible to teach TDD when someone doesn’t know basic programming principles. They can’t talk about APIs when students didn’t know HTTP. They can’t walk through algorithms when students can’t control nested loops.
  • Related to the first issue, students didn’t retain pace with the lectures. About half the students stopped attending the live lectures after the first week. Though totality lectures were recorded, few made an application to bear up for lost time and instead elected to depart at their own pace. By the halt of the month-long course, only a few students were still attending the live lectures.
  • The above two problems forced us early on to elect if they cared about students’ comprehension at the halt of courses. If they didn’t, the solution would subsist easier: they could just sell recorded videos and content for a fixed cost and focus their energies on marketing the content. On the other hand, if they did supervision about comprehension afterwards, we’d possess to find another teaching format because while the credence of live lectures with quizzes seemed trustworthy in theory, in practice, most people don’t possess the discipline to finish a rigorous course. And without the threat of withholding a credential, they couldn’t attain anything to constrain people to account for up.

    These problems besides forced us to reflect difficult about who their audience was. If companies relish Dev Bootcamp were able to train people for towering paying jobs, why couldn’t they attain the same, if only they selected the prerogative students? My previous suffer as an Engineering Manager told me that companies are willing to pay $15,000 to $30,000+ as a referral fee for qualified candidates. Couldn’t they monetize that halt if they could find and train trustworthy students? This line of thinking only made things more confusing, because if they retain pushing on that logic, wouldn’t it subsist easier to just become a recruiting company? Why bother doing totality the difficult work of trying to train unprepared people when they can just filter for the best? That seemed relish a more viable business, especially since totality the startup literature says to saturate businesses instead of individual users wherever you can.

    Our initial stab at teaching people programming yielded some stars who landed noteworthy jobs, but that was, as is trustworthy for most education institutions, a result of selection prejudice as opposed to their unbelievable training methods. The choices in front of us were either to 1) design out a way to bear money and give up on making sure students actually understood the material, or 2) design out a way to better train people for comprehension and not worry about optimizing for revenue.

    We made a few critical decisions then that they still adhere to today:

  • Students are their customers, not employers. By eliminating employers as a practicable revenue source, it brought clarity to what they were supposed to do. One of the things they wanted to attain was to assist people, not only to bear money for ourselves. After all, they had just quit towering paying jobs to attain something meaningful together. Helping employers didn’t appear very meaningful to us personally and while they were ok with that being a side consequence of producing noteworthy programmers, they didn’t want to incentivize ourselves to become a recruiting company.
  • We decided to not engage venture funding. Though it may possess been a bit early in their lifecycle to bear that decision, they felt that training companies attain not possess a significant viral first-mover advantage. Instead, the advantage was in long-term reputation. Sure, it’d subsist practicable to over-promise and over-hype the marketing in the short-term, but their hypothesis was that over time the lack of results will entangle up with the hype. They had decided to dedicate their entire thirties to this experiment, and they felt that this long-term mindset could subsist an advantage in the education space. It’s keen that Shereef, Roshan, and Dave opted for the antithetical route with their companies and took on venture funding.
  • The consequences of those decisions significantly focused their energy.

    By identifying students as their customers, they aligned ourselves with students and started to focus on pedagogy and comprehension, rather than throughput and conversion. It besides meant we’re a B2C company and not a B2B company. This had implications to their processes. For example, they stopped doing sales calls to employers to try to gain them to purchase licenses in bulk. Instead, they took time to possess calls with every prospective student.

    By going the bootstrapped route, they decided on a low-burn long-term fiscal plan, which usually meant sacrificing marketing for curriculum development. In their hypothesis, there’s no rush to gain to market, and it’s more critical to protect Launch School’s reputation by always doing “the prerogative thing for the student”. Venture-backed companies possess a “fail fast, fail often” mentality where growth rules above all. But in education, “failing” means negatively affecting students’ lives. They weren’t restful with purposefully hurting even a small group of students as fraction of the industry plan.

    2013–2014: Tealeaf Academy

    We continued running their synchronous cohorts and the problematic patterns kept repeating cohort after cohort. They took everything they learned and decided to change their curriculum in a brace of critical ways:

  • From synchronous to asynchronous (aka, self-paced). Instead of relying on live lectures that were sparsely attended, we’d rush to recordings that students could watch at any time.
  • From one 1-month long course, they moved to 3 courses that would engage roughly 4 months in total. The courses would start from the ground up, teaching basic programming principles to start, then edifice up to web evolution basics, and finally to totality the advanced concepts employers wanted.
  • These two changes made a huge contrast and students understood this sequence of courses much better. Instead of feeling overwhelmed in the first week, students could complete lectures and assignments on their own schedule. They didn’t give too much thought to the pricing structure and continued to sell the courses at a fixed cost per course.

    Even with the original self-paced 3-course sequence, results still varied widely. Some graduates got jobs that paid over $100k, and others who finished totality 3 courses said they didn’t learn a thing. They posted the $100k student on their testimonials, but it felt relish selection prejudice and not actual education for all. It felt that despite their efforts to avoid becoming a recruiting company, they just ended up creating a recruiting company with a 3-course filter.

    The whole point of charging students and forgoing funding was so they can align ourselves with students and attain the prerogative thing for students. So how can someone pay over $2,000 and spend over 4 months, and then direct they didn’t learn anything? Even if it was a small number of students, that was still a crushing result for us. They couldn’t let it depart and write it off as people being unprepared.

    We decided to zoom in on the problem and try to understand the core of the issue. They participated in countless 1on1 sessions with students who were struggling and began noticing patterns. They would pair with students who were struggling in course 3 and descry that what they were struggling with was not the advanced topic, but fundamentals. They couldn’t build an API not because they couldn’t intellectually understand the concept of an API, but because they didn’t know how HTTP worked. It had nothing to attain with intellectual ability, but everything attain with understanding of prerequisite knowledge. When they asked “don’t you bethink HTTP from course 1?”, they’d direct something to the consequence of “sure, benevolent of, but I went through that fraction pretty fast, and to subsist honest, it’s still a shrimp fuzzy”. After seeing this over and over, they realized that they were missing a critical component in their courses: assessments.

    After teaching people for 2 years, they learned what teachers across the world possess known for centuries: you must possess some test of mastery to demonstrate comprehension.

    Upton Sinclair once said, “It is difficult to gain a man to understand something, when his salary depends on his not understanding it.” They fell into this trap by not thinking carefully about how their pricing fitting with their pedagogy. They never seriously thought about adding rigorous assessments because it meant that less students would enroll in and pay for subsequent courses. They were financially incentivizing ourselves to usher students to subsequent courses without regard to mastery, which is in direct combat with their mastery-based values. They charged per course, so adding assessments would possess resulted in less revenue. The key lesson they took away from this observation was: subsist awake of how pricing introduces natural blindspots to your company or product.

    2015: Lessons Learned

    Having taught people for over 2 years at this point, they had enough information to depart back to the lab and build a curriculum from the ground up anew. They spent the next year studying, researching and debating about what a noteworthy training program looked like. Over and over, they institute ourselves constantly trapped by incompatible goals. For example, they wanted a democratic learning program that could cater to all, but how attain you reconcile that goal with the crave to drive people to towering paying jobs? You either possess to give up the towering paying jobs or you possess to filter based on experience. If you only possess a 4 or 6 month timeframe, what topics attain you cover and how attain you bear sure people are following along? Is it ok if only the top 10% or 20% understand the material at the end?

    To address these incompatible learning goals, they started from their own first principles by thinking about how we’re different, what their core beliefs were, and their personal stance on learning and comprehension. One credence that came up over and over in their research and discussions was operating for the *long-term*.

    If they engage a long-term perspective in their industry operations, then it’d subsist practicable to besides engage a long-term perspective on their pedagogical approach for the curriculum. They can’t possess a company that’s focused on chasing quarterly revenue results and reconcile that with a long-term curriculum. The company’s vision and the pedagogy must subsist aligned. After realizing that, they made an critical decision: they decided to not only spend their thirties on this, but to spend the repose of their careers on this project. That seems melodramatic and conjures up images of a sworn blood oath under a replete moon, but it wasn’t a difficult conclusion at totality and they made it fairly quickly and unceremoniously. That’s because 1) they didn’t possess any other trustworthy ideas in the pipeline, 2) they believe that working on this problem will positively strike the world, 3) they believe in each other and don’t want to work on separate things, and 4) teaching online allows us to engage with a worldwide community of students, which brings a inescapable joy to the project. They didn’t possess any reason to stop, and they thought that by focusing on decades in the future, they could spend that perspective to their advantage.

    Suddenly after that shift in perspective, they could descry how a willingness to reflect about 10, 20 years into the future allowed us to unlock long-term value, both for us as a industry and their students. While there were a lot of short-term incompatibilities between learning goals and industry goals, these issues melted away when considered in the span of years and decades. Suddenly they could focus on skills to final a career, rather than chase short-term fads. They finally institute a way to align personal, business, and student goals.

    Just relish how a long-simmering programming mystify may forward into more focus as one spends more time digesting it, the education mystify began to unfold for us as they shifted into long-term thinking. With the long-term perspective as their north star, they came up with the following values for their industry and learning pedagogy:

  • Mastery of fundamentals first.
  • No time circumscribe for each course.
  • Assessments to test mastery.
  • Pedagogy-led pricing.
  • Don’t focus on short-term revenue.
  • All these ideas taken together formed the foundation for their Mastery-based Learning pedagogy at Launch School.

    2016: Launch School

    It took us a year to build the original curriculum, and at the halt of 2015, they launched Launch School. They didn’t possess proof that this original curriculum would subsist good; it seemed prerogative based on their suffer and values, but since they just started, they didn’t possess any concrete results to show. They asked prospective students to trust the process and asked if learning fundamentals to mastery made intuitive sense. They didn’t attain any market research and built the original curriculum based off of their own standards of excellence, so they weren’t sure how people would react. Would they search for at their proposal of learning indefinitely and then compare it with a 3-month bootcamp and laugh at us? Would they agree with us that the issue with learning advanced topics and frameworks was totality about understanding fundamentals? The current marketplace was replete of hype about turning around a six-figure job after a few months. How would people receive the credence of potentially learning for a few years?

    Fortunately for us, some people chose to trust the process and started learning with us, from fundamentals with mastery.

    2017: Capstone

    By focusing on fundamentals, they felt they were setting up students for long-term success. But they still had the “last mile” problem to solve to demonstrate that there’s a quantitative contrast between those who took time to learn fundamentals vs those who didn’t. After all, if the results between learning fundamentals for 2 years and cramming frameworks for 2 months are the same, why bother with the fundamentals?

    Towards the halt of 2016, they were able to engage some of their Launch School students and locality them into an fierce instructor-led program to descry if they could address the “last mile” problem. They created Capstone, a finishing program where students could apply their already-mastered fundamentals to more intricate engineering problems. They wanted to account for the world what’s practicable when you engage years to really learn something well by putting their Capstone graduates into the marketplace. They spent most of 2017 running Capstone cohorts and observing their performance in the most competitive markets in the United States.

    2018: Results and Outcomes

    Finally in 2018, they were able to showcase the results so far. Because it took a few years for us to wrap their head around the problem, and then a few more years for students to complete their curriculum, they are only seeing quantitative results now in 2018. Of course, they had many small victories along the way with many of their students saying their courses changed their lives, but teaching fundamentals for years besides meant taking us farther away from concrete results. Now that they possess them, the results are astounding; descry for yourself.

    Why doesn’t anyone else attain Mastery-based Learning?

    To address the question that initially triggered this article, I reflect they were the only ones who arrived at Mastery-based Learning because of the following.

    We’re bootstrapped.

    Other programs focus on financing and pricing innovation, partnerships, scholarships, marketing, government sponsorships, accreditation/credentialism, industry process innovation, niche audience segmentation, but nonexistent appear interested in pedagogical innovation. I believe that they were able to focus squarely on pedagogy because they kept expanding their time horizon, which wouldn’t possess been practicable with venture funding. Had they taken investors’ money, we’d possess been pressured to find a path to hyper-growth before the money ran out. This is why so many funded coding bootcamps are under stress and can’t innovate on one of the most critical attributes for educational companies: their pedagogy.

    Quality over data.

    I relish to reflect I’m a data-driven person, but many operators act larger than they are. Most small education companies are not operating at the scale of Amazon (the archetype for the soul-less numbers driven company), and yet they spend numbers to override values. Numbers and data are important, but you must possess some opinions on quality regarding your craft that you can’t compromise on regardless of what the numbers say. Had they followed the difficult logic of numbers from their first year of teaching, they would’ve ended up a recruiting company because that’s what the data says employers wanted. There are besides things they won’t do, no matter what the data says. For example, they just plainly rebuff to “fail fast, and fail often” because it hurts people (also, they bear enough honest mistakes that they don’t necessity a company philosophy to propel for more). I bethink first hearing about this concept and thought “that’s a noteworthy hack for startup founders”. But when you’re on the receiving halt of this ideology as a customer, you reflect “what a bunch of amateurs and assholes”. In order to attain the prerogative thing, you possess to possess an sentiment around quality. If you don’t yet possess one, it’s critical to rush slowly and design it out until you do. Following a 100% numbers driven analysis, no one would arrive at Mastery-based Learning.

    Have core values.

    A lot of people treat starting a industry as a treasure hunt for revenue. In the course of running a business, many decisions forward down to this choice: bear money or help quality. It seems counterintuitive, shouldn’t the higher quality product bear more money? In industries where results are not obvious or delayed by months and years, it’s very practicable to over-promise and lead with marketing. In such industries, it’s much easier to first bear money and then design it out later (another venture-backed mantra: “fake it until you bear it”). One major lesson I learned starting Launch School was in learning more about myself. For example, I learned that there wasn’t one or two lines, but lots of lines I wasn’t willing to cross to bear money. I learned about who I was, and who I wanted to become and it’s not a noteworthy entrepreneur or the founder of a multi-million dollar company. For me, it’s about trying to build something worthwhile that lasts as long as possible. It’s about enjoying the daily process of work and doing something positive for the world and working with people I savor being around. Just as towering salaries are actually not the halt goal for students at Launch School (they are a side consequence of learning to mastery), revenue is not the halt goal of the industry side of Launch School — it is a side consequence of becoming a meaningful long-term organization. I believe that this perspective is what helped us to unlock the long-term value behind Mastery-based Learning.


    The Best Self-Service industry Intelligence (BI) Tools of 2018 | killexams.com actual questions and Pass4sure dumps

    Analytics Beyond Spreadsheets

    For many years, Microsoft excel and other spreadsheets were the tools of election for industry professionals who were looking to visualize their data. But spreadsheets had their limits for many industry intelligence (BI)-related tasks. Even today, trying to creating charts analyzing intricate datasets in excel can still subsist frustrating. Sometimes you start with the wrong benevolent of data, for example, or you may not know how to exploit the spreadsheet to create the data visualization{{/ZIFFARTICLE} you need. On the other hand, the rising tide of data democratization is giving everyone in an organization access to corporate data. The necessity has arisen for effectual tools that people of totality skill levels can spend to bear sense of the wealth of information created by businesses every single day.

    Spreadsheets besides plunge down when the data isn't well-structured or can't subsist sorted out in spruce rows and columns. And, if you possess millions of rows or very sparse matrices, then the data in a spreadsheet can subsist painful to enter and it can subsist difficult to visualize your data. Spreadsheets besides possess issues if you are trying to create a report that spans multiple data tables or that mixes in Structured Query Language (SQL)-based databases, or when multiple users try to maintain and collaborate on the very spreadsheet.

    A spreadsheet containing up-to-the-minute data can besides subsist a problem, particularly if you possess exported graphics that necessity to subsist refreshed when the data changes. Finally, spreadsheets aren't trustworthy for data exploration; trying to spot trends, outlying data points, or counterintuitive results is difficult when what you are looking for is often hidden in a long row of numbers.

    While spreadsheets and self-service BI tools both bear spend of tables of numbers, they are really acting in different arenas with different purposes. A spreadsheet is first and foremost a way to store and panoply calculations. While some spreadsheets can create very sophisticated mathematical models, at their core it is totality about the math more than the model itself.

    This is totality a long-winded way of saying that when businesses spend a spreadsheet, they are actively sabotaging themselves and their aptitude to consistently gain valuable insights from their data. BI tools are specficially designed to assist businesses better understand their data, and can prove to subsist a huge capitalize to those upgrading from what a limited spreadsheet can do.

    What Is industry Intelligence?

    Defining BI is tricky. When you examine what it does and why companies spend it, it can start to sound vague and nebulous. After all, many different kinds of software offer analytics features, and totality businesses want to improve. Understanding what a BI is or isn't can subsist unclear.

    BI is an umbrella term meant to cover totality of the activities necessary for a company to turn raw information into actionable knowledge. In other words, it's a company's efforts to understand what it knows and what it doesn't know of its own being and operations. The ultimate goal is being able to multiply profits and sharpen its competitive edge.

    Framed that way, BI as a concept has been around as long as business. But that concept has evolved from early basics [like Accounts Payable (AP) and Accounts Receivable (AR) reports and customer contact and compress information] to much more sophisticated and nuanced information. This information ranges across everything from customer behaviors to IT infrastructure monitoring to even long-term fixed asset performance. Separately tracking such metrics is something most businesses can attain regardless of the tools employed. Combining them, especially disparate results from metrics normally not associated with one another, into understandable and actionable information, well, that's the craft of BI. The future of BI is already shaping up to simultaneously broaden the scope and variety of data used and to sharpen the micro-focus to ever finer, more granular levels.

    BI software has been instrumental in this steady progression towards more in-depth erudition about the business, competitors, customers, industry, market, and suppliers, to appellation just a few practicable metric targets. But as businesses grow and their information stores balloon, the capturing, storing, and organizing of information becomes too great and intricate to subsist entirely handled by mere humans. Early efforts to attain these tasks via software, such as customer relationship management (CRM) and enterprise resource planning (ERP), led to the formation of "data silos" wherein data was trapped and useful only within the confines of inescapable operations or software buckets. This was the case unless IT took on the task of integrating various silos, typically through painstaking and highly manual processes.

    While BI software still covers a variety of software applications used to dissect raw data, today it usually refers to analytics for data mining, analytical processing, querying, reporting, and especially visualizing. The main contrast between today's BI software and gargantuan Data analytics is mostly scale. BI software handles data sizes typical for most organizations, from small to large. gargantuan Data analytics and apps ply data analysis for very great data sets, such as silos measured in petabytes (PBs).

    Self-Service BI and Data Democratization

    The BI tools that were common half a decade or more ago required specialists, not just to spend but besides to interpret the resulting data and conclusions. That led to an often inconvenient and fallible filter between the people who really needed to gain and understand the business—the company conclusion makers—and those who were gathering, processing, and interpreting that data—usually data analysts and database administrators. Because being a data specialist is a demanding job, many of these folks were less well-versed in the actual workings of the industry whose data they were analyzing. That led to a focus on data the company didn't need, a misinterpretation of results, and often a series of "standard" reporting that analysts would elope on a scheduled basis instead of more ad hoc intelligence gathering and interpretation, which can subsist highly valuable in fast-moving situations.

    This problem has led to a growing original trend among original BI tools coming onto the market today: that of self-service BI and data democratization. The goal for much of today's BI software is to subsist available and usable by anyone in the organization. Instead of requesting reports or queries through the IT or database departments, executives and conclusion makers can create their own queries, reports, and data visualizations through self-service models, and connect to disparate data both within and outside the organization through prebuilt connectors. IT maintains overall control over who has access to which tools and data through these connectors and their management tool arsenal, but IT no longer acts as a bottleneck to every query and report request.

    As a result, users can engage advantage of this distributed BI model. Key tools and critical data possess moved from a centralized and difficult-to-access architecture to a decentralized model that merely requires access credentials and familiarity with original BI software. This results in a whole original benevolent of analysis becoming available to the organization, namely, that of experienced, front-line industry people who not only know what data they necessity but how they necessity to spend it.

    The emerging crop of BI tools totality work difficult at developing front-end tools that are more intuitive and easier to spend than those of older generations—with varying degrees of success. However, that means a key criteria in any BI tool purchasing conclusion will subsist to evaluate who in the organization should access such tools and whether the tool is appropriately designed for that audience. Most BI vendors bespeak they're looking for their tool suites to become as ubiquitous and effortless to spend for industry users as typical industry collaboration tools or productivity suites, such as Microsoft Office. nonexistent possess gotten quite that far yet in my estimation, but some are closer than others. To that end, these BI tool suites tend to focus on three core types of analytics: descriptive (what did happen), prescriptive (what should chance now), and predictive (what will chance later).

    What Is Data Visualization?

    In the context of BI software, data visualization is a snappy and effectual mode of transferring information from a machine to a human brain. The credence is to locality digital information into a visual context so that the analytic output can subsist quickly ingested by humans, often at a glance. If this sounds relish those pie and bar charts you've seen in Microsoft Excel, then you're right. Those are early examples of data visualizations.

    But today's visualization forms are rapidly evolving from those traditional pie charts to the stylized, the artistic, and even the interactive. An interactive visualization comes with layered "drill downs," which means the viewer can interact with the visual to attain more granular information on one or more aspects incorporated in the bigger picture. For example, original values can subsist added that will change the visualization on the fly, or the visualization is actually built on rapidly changing data that can turn a static visual into an animation or a dashboard.

    The best visualizations attain not seek artistic awards but instead are designed with role in mind, usually the quick and intuitive transfer of information. In other words, the best visualizations are simple but powerful in clearly and directly delivering a message. High-end visuals may search for impressive at first glance but, if your audience needs assist to understand what's being conveyed, then they've ultimately failed.

    Most BI software, including those reviewed here, comes with visualization capabilities. However, some products offer more options than others so, if advanced visuals are key to your BI process, then you'll want to closely examine these tools. There are besides third-party and even free data visualization tools that can subsist used on top of your BI software for even more options.

    Products and Testing

    In this review roundup, I tested each product from the perspective of a industry analyst. But I besides kept in mind the viewpoint of users who might possess no familiarity with data processing or analytics. I loaded and used the very data sets and posed the very queries, evaluating results and the processes involved.

    My level was to evaluate cloud versions alone, as I often attain analysis on the sail or at least on a variety of machines, as attain legions of other analysts. But, in some cases, it was necessary to evaluate a desktop version as well or instead of the cloud version. One illustration of this is Tableau Desktop, a favorite tool of Microsoft excel users who simply possess an affinity for the desktop tool (and who just rush to the cloud long enough to share and collaborate).

    I ended up testing the Microsoft Power BI desktop version, too, on a Microsoft representative's recommendation because, as the rep said, "the more robust data prep tools are there." Besides, said the rep, "most users prefer the desktop tool over a web tool anyway." Again, I don't doubt Microsoft's pretense but that does appear weird to me. I've heard it said that desktop tools are preferred when the data is local as the process feels faster and easier. But seriously, how much data is truly local anymore? I suspect this odd desktop tool preference is a bit more personal than fact-based, but to each his own.

    Then there's Google Analytics, a absolute cloud player. The tool is designed to dissect website and mobile app data so it's a different critter in the BI app zoo. That being the case, I had to deviate from using my test data set and queries, and instead test it in its natural habitat of website data. Nonetheless, it's the processes that are evaluated in this review, not the data.

    While I didn't test any of these tools from a data scientist's role, I did mention advanced capabilities when I institute them, simply to let buyers know they exist. IBM Watson Analytics is one tool with the aptitude to extend to highly advanced features and was besides one of the easiest to spend upfront. IBM Watson Analytics is well-suited for industry analysts and for widespread data democratization because it requires little, if any, erudition of data science. Instead, it works well by using natural language and keywords to configuration queries, a characteristic that can bear it valuable to practically anyone. It's highly intuitive, very powerful, and effortless to learn. Microsoft Power BI is a strong second as it, too, is powerful while besides familiar, certainly to any of the millions of Microsoft industry users. However, there are several other powerful and intuitive apps in this lineup from which to choose; they totality possess their own pros and cons. We'll subsist adding even more in the coming months.

    One thing to watch out for during your evaluations of these products is that many don't yet ply streaming data. For many users, that won't subsist a problem in the immediate future. However, for those involved with analyzing industry processes as they happen, such as website performance metrics or customer behavior patterns, streaming data can subsist invaluable. Also, the Internet of Things (IoT) will drive this issue in the near future and bear streaming data and streaming analytics a must-have feature. Many of these tools will possess to up their game accordingly so, unless you want to jump ship in a year or two, it's best to reflect ahead when considering BI and the IoT.

    BI and gargantuan Data

    Another locality in which self-service BI is taking off is in analyzing gargantuan Data. This is a newer evolution in the database space but it's driving tremendous growth and innovation. The appellation is an apt descriptor because gargantuan Data generally refers to huge data sets that are simply too gargantuan to subsist managed or queried with traditional data science tools. What's created these behemoth data collections is the explosion of data-generating, tracking, monitoring, transaction, and social media tools (to appellation a few) that possess become so common over the final several years.

    Not only attain these tools generate loads of original data, they besides often generate a original benevolent of data, namely "unstructured" data. Broadly speaking, this is simply data that hasn't been organized in a predefined way. Unlike more traditional, structured data, this benevolent of data is hefty on text (even free-form text) while besides containing more easily defined data, such as dates or credit card numbers. Examples of apps that generate this benevolent of data involve the customer behavior-tracking tools you spend to descry what your customers are doing on your e-commerce website, the piles of log and event files generated from some smart devices (such as alarms and smart sensors), and broad-swath social media tracking tools.

    Organizations deploying these tools are being challenged not only by a sudden deluge of unstructured data that quickly strains storage resources [think beyond terabytes (TB) into the PB and even exabyte (EB) range] but, even more importantly, they're finding it difficult to query this original information at all. Traditional data warehouse tools generally weren't designed to either manage or query unstructured data. original data storage innovations such as data lakes are emerging to solve for this need, but organizations still relying exclusively on traditional tools while deploying front-line apps that generate unstructured data often find themselves sitting on mountains of data they don't know how to leverage.

    Enter gargantuan Data analysis standards. The golden standard here is Hadoop, which is an open-source software framework that Apache specifically designed to query great data sets stored in a distributed mode (meaning, in your data center, the cloud, or both). Not only does Hadoop let you query gargantuan Data, it lets you simultaneously query both unstructured as well as traditional structured data. In other words, if you want to query totality of your industry data for maximum insight, then Hadoop is what you need.

    You can download and implement Hadoop itself to effect your queries, but it's typically easier and more effectual to spend commercial querying tools that employ Hadoop as the foundation of more intuitive and full-featured analysis packages. Notably, most of the tools reviewed here, including Chartio, IBM Watson Analytics, Microsoft Power BI, and Tableau Desktop, totality support this. However, each requires varying levels of configuration or even add-on tools to attain so—with IBM, Microsoft, and Tableau offering exceptionally abysmal capabilities. However, both IBM and Microsoft will still anticipate customers to utilize additional tools around aspects such as data governance to ensure optimal performance.

    Finding the prerogative BI Tool

    Given the issues spreadsheets can possess when used as ad hoc BI tools and how firmly ingrained they are in their psyches, finding the prerogative BI tool isn't a simple process. Unlike spreadsheets, BI tools possess major differences when it comes to how they consume data inputs and outputs and exploit their tables. Some tools are better at exploration than analysis, and some require a fairly steep learning curve to really bear spend of their features. Finally, to bear matters worse, there are dozens if not hundreds of such tools on the market today, with many vendors willing to pretense the self-serve BI label even if it doesn't quite fit.

    Getting the overall workflow down with these tools will engage some study and discussion with the people you'll subsist designating as users. Tableau Desktop and Microsoft Power BI, for example, will start users out with the desktop version to build visualizations and link up to various data sources. Once you possess this together, you can start sharing those results online or across your organization's network. With others, such as Chartio or Google Analytics, you start in the cloud and stay there.

    In recent years, companies possess been taking advantage of the wide selection of online learning platforms out there to train their employees on using these platforms. As intuitive as these platforms may be, it is critical to bear sure that your employees actually know how to spend these BI platforms so that you can bear sure your investment was worthwhile. There are many ways of approaching this, but using the prerogative online learning platform might subsist a trustworthy locality to start looking.

    Given the wide cost range of these products, you should segment your analytics needs before you bear any buying decision. If you want to start out slowly and inexpensively, then the best route is to try something that offers significant functionality for free, such as Microsoft Power BI. Such tools are very affordable and bear it effortless to gain started. Plus, they tend to possess great ecosystems of add-ons and partners that can subsist a cost-effective replacement for doing BI inside a spreadsheet. Tableau Desktop still has the largest collection of charts and visualizations and the biggest partner network, though both IBM Watson Analytics and Microsoft Power BI are catching up fast.

    IBM Watson Analytics scored the highest, and Microsoft Power BI and Tableau Desktop scored the next highest in their roundup. However, totality three products received their Editors' election award. Tableau Desktop may possess a gargantuan cost tag depending on which version you elect but, as previously mentioned, it has an exceptionally great and growing collection of visualizations plus a manageable learning curve if you're willing to pledge some application to it. Microsoft Power BI and Tableau Desktop besides possess great and growing collections of data connectors, and both Microsoft and Tableau possess their own sizable communities of users that are vocal about their wants and needs. This can carry a lot of weight with the vendors' evolution teams so it's a trustworthy credence to spend some time looking through those community forums to gain an credence where these companies are headed.

  • Pros: Extremely user-friendly. grotesque automatic report generation. Impressive support availability.

    Cons: Automated reports can quickly become defaults. Steep learning curve that might confuse beginners.

    Bottom Line: Zoho Reports is a solid option for general industry users who might not subsist knowledgeable in analytics software. It's besides available at an attractive price.

    Read Review
  • Pros: Accessible user interface. Smart guidance features. Impressively snappy analytics. Robust natural language querying.

    Cons: Unable to attain real-time streaming analytics.

    Bottom Line: IBM Watson Analytics is an exceptional industry intelligence (BI) app that offers a strong analytics engine along with an excellent natural language querying tool. This is one of the best BI platforms you'll find and easily takes their Editors' election honor.

    Read Review
  • Pros: Extremely powerful platform with a wealth of data source connectors. Very user-friendly. Exceptional data visualization capabilities.

    Cons: Desktop and web versions divide data prep tools. Refresh cycle is limited on free version.

    Bottom Line: Microsoft Power BI earns their Editors' election reverence for its impressive usability, top-notch data visualization capabilities, and superior compatibility with other Microsoft Office products.

    Read Review
  • Pros: mammoth collection of data connectors and visualizations. User-friendly design. Impressive processing engine. mature product with a great community of users.

    Cons: replete mastery of the platform will require substantial training.

    Bottom Line: Tableau Desktop is one of the most mature offerings on the market and that shows in its feature set. While it has a steeper learning curve than other platforms, it's easily one of the best tools in the space.

    Read Review
  • Pros: Bottlenecks are eliminated thanks to in-chip processing. Impressive natural language query in third-party applications.

    Cons: Might subsist too difficult for self-service industry intelligence (BI). Analytics process still needs to subsist ironed out. Natural language capability can subsist limited.

    Bottom Line: Sisense is a complete platform that should subsist common for experienced BI users. It may plunge short for beginners, however.

    Read Review
  • Pros: Wide range of connectors. Impressive sharing features. Limitless data storage.

    Cons: User interface is not intuitive. Steep learning curve. Unwelcoming to original analysts.

    Bottom Line: Domo isn't for newcomers but for companies that already possess industry intelligence (BI) suffer in their organization. Domo's a powerful BI tool with a lot of data connectors and solid data visualization capabilities.

    Read Review
  • Pros: Exceptional platform for website and mobile app analytics.

    Cons: Customer support has way too much automation. Focus on marketing and advertising can subsist frustrating to users. Relies mostly on third parties for training.

    Bottom Line: Due to its brand recognition and the fact that it's free, Google Analytics is the biggest appellation in website and mobile app intelligence. It has a steep learning curve but it is an awesome industry intelligence tool.

    Read Review
  • Pros: Designed with general industry users in mind. Solid revert on investment.

    Cons: The data you can spend is limited. Needs additional platform to connect.

    Bottom Line: The Salesforce Einstein Analytics Platform is designed for customer, sales, and marketing analyses, although it can server other needs, too. Its powerful analytics capabilities along with its solid natural language querying functionality and a wide array of partners bear it an attractive offering.

    Read Review
  • Pros: Real-time analytics for Internet of Things (IoT) and streaming data features. Massive ecosystem with plenteous extenders. Responsive pages bear mobile publishing easiest. Impressive storytelling paradigm. Centralized view with consolidated analytics.

    Cons: Data prep features are lacking. Confusing toolbar design. Not friendly for beginners.

    Bottom Line: If your industry already uses SAP's HANA database platform or some of its other back-end industry platforms, then SAP Analytics Cloud is a powerful, well-priced choice. But subsist warned that there's a steep learning curve and a famed dependence on other SAP products for replete functionality.

    Read Review
  • Pros: Impressive processing engine. Powerful query optimization on SQL. Entirely web-based. intricate queries are handled very well.

    Cons: Poorly designed user interface. Steep learning curve.

    Bottom Line: Chartio excels at edifice a powerful analytics platform that experienced industry intelligence (BI) users will appreciate. Those original to BI, however, will find it very difficult to use.

    Read Review
  • Pros: Very abysmal SQL modeling ability. Uses Git for version management and collaboration.

    Cons: Very expensive. Not for small teams.

    Bottom Line: Looker is a noteworthy self-service industry intelligence (BI) tool that can assist unify SQL and gargantuan Data management across your enterprise.

    Read Review
  • Pros: Custom access roles. Solid collection of public data online.

    Cons: intricate pricing is a deterrent.

    Bottom Line: Qlik Sense Enterprise Server is a self-service industry intelligence (BI) tool that delivers the best collection of user access roles among the BI tools they tested, and besides demonstrates a promising start towards integrating Data-as-a-Service (DaaS).

    Read Review
  • Pros: One of the largest collections of data connectors. Many granular access roles.

    Cons: No free trial available. Training webinars can subsist costly.

    Bottom Line: The company's Focus query language is showing its age but Information Builders' self-service industry intelligence (BI) tool WebFocus nevertheless has some powerful analysis features.

    Read Review
  • Pros: Very effortless to gain started. Nice team management and collaboration features.

    Cons: The cloud version has a subset of features institute in Windows version. Online documentation could subsist improved.

    Bottom Line: While Tibco is still making the transition from a desktop to a cloud software vendor, its self-service industry intelligence (BI) tool Tibco Spotfire is a noteworthy way to start visualizing your excel data.

    Read Review
  • Pros: Excellent analytical support for Intuit QuickBooks. Very effortless setup.

    Cons: Installation and setup is a bit of chore. No support for Intuit QuickBooks' online versions.

    Bottom Line: Clearify QQube is the best self-service industry intelligence (BI) tool for in-depth analysis of your Intuit QuickBooks files, though you'll necessity to search for elsewhere for broader BI tasks.

    Read Review

  • The customized, digitized, have-it-your-way economy Mass customization will change the way products are made-- forever. | killexams.com actual questions and Pass4sure dumps

    The customized, digitized, have-it-your-way economy Mass customization will change the way products are made-- forever.

    (FORTUNE Magazine) – A silent revolution is stirring in the way things are made and services are delivered. Companies with millions of customers are starting to build products designed just for you. You can, of course, buy a Dell computer assembled to your exact specifications. And you can buy a pair of Levi's gash to fitting your body. But you can besides buy pills with the exact blend of vitamins, minerals, and herbs that you like, glasses molded to fitting your face precisely, CDs with music tracks that you choose, cosmetics mixed to match your skin tone, textbooks whose chapters are picked out by your professor, a loan structured to meet your fiscal profile, or a night at a hotel where every employee knows your favorite wine. And if your child does not relish any of Mattel's 125 different Barbie dolls, she will soon subsist able to design her own.

    Welcome to the world of mass customization, where mass-market goods and services are uniquely tailored to the needs of the individuals who buy them. Companies as diverse as BMW, Dell Computer, Levi Strauss, Mattel, McGraw-Hill, Wells Fargo, and a slew of leading Web businesses are adopting mass customization to maintain or obtain a competitive edge. Many are just nascence to dabble, but the direction in which they are headed is clear. Mass customization is more than just a manufacturing process, logistics system, or marketing strategy. It could well subsist the organizing principle of industry in the next century, just as mass production was the organizing principle in this one.

    The two philosophies couldn't clash more. Mass producers prescribe a one-to-many relationship, while mass customizers require interminable dialogue with customers. Mass production is cost-efficient. But mass customization is a resilient manufacturing technique that can slash inventory. And mass customization has two huge advantages over mass production: It is at the service of the customer, and it makes replete spend of cutting-edge technology.

    A whole list of technological advances that bear customization practicable is finally in place. Computer-controlled factory paraphernalia and industrial robots bear it easier to quickly readjust assembly lines. The proliferation of bar-code scanners makes it practicable to track virtually every fraction and product. Databases now store trillions of bytes of information, including individual customers' predilections for everything from cottage cheese to suede boots. Digital printers bear it a cinch to change product packaging on the fly. Logistics and supply-chain management software tightly coordinates manufacturing and distribution.

    And then there's the Internet, which ties these disparate pieces together. Says Joseph Pine, author of the pioneering book Mass Customization: "Anything you can digitize, you can customize." The Net makes it effortless for companies to rush data from an online order configuration to the factory floor. The Net makes it effortless for manufacturing types to communicate with marketers. Most of all, the Net makes it effortless for a company to conduct an ongoing, one-to-one dialogue with each of its customers, to learn about and respond to their exact preferences. Conversely, the Net is besides often the best way for a customer to learn which company has the most to offer him--if he's not tickled with one company's wares, nearly consummate information about a competitor's is just a mouse click away. Combine that with mass customization, and the nature of a company's relationship with its customers is forever changed. Much of the leverage that once belonged to companies now belongs to customers.

    If a company can't customize, it's got a problem. The Industrial Age model of making things cheaper by making them the very will not hold. Competitors can copy product innovations faster than ever. Meanwhile, consumers claim more choices. Marketing guru Regis McKenna declares, "Choice has become a higher value than brand in America." The largest market shares for soda, beer, and software attain not belong to Coca-Cola, Anheuser-Busch, or Microsoft. They belong to a category called Other. Now companies are trying to bear a unique Other for each of us. It is the ratiocinative culmination of markets' being chopped into finer and finer segments. After all, the ultimate niche is a market of one.

    The best--and most famous--example of mass customization is Dell Computer, which has a direct relationship with customers and builds only PCs that possess actually been ordered. Everyone from Compaq to IBM is struggling to copy Dell's model. And for trustworthy reason. Dell passed IBM final quarter to pretense the No. 2 spot in PC market share (behind Compaq). While other computer manufacturers struggle for profits, Dell keeps reporting record numbers; in its most recent quarter the company's sales were up 54%, while earnings soared 62%. No wonder Michael Dell has become the poster boy of the original economy. As Pine says, "The closest person they possess to Henry Ford is Michael Dell."

    Dell's triumph is not so much technological as it is organizational. Dell keeps margins up by keeping inventory down. The company builds computers from modular components that are always readily available. But Dell doesn't want to store tons of parts: Computer components decline in value at a rate of about 1% a week, faster than just about any product other than sushi or losing lottery tickets. So the key to the system is ensuring that the prerogative parts and products are delivered to the prerogative locality at the prerogative time.

    To attain this, Dell employs sophisticated logistics software, some developed internally, some made by i2 Technologies. The software takes info gathered from customers and steers it to the parts of the organization that necessity it. When an order comes in, the data collected are quickly parsed out--to suppliers that necessity to rush over a shipment of difficult drives, say, or to the factory floor, where assemblers locality parts together in the customer's desired configuration. "Our goal," says vice chairman Kevin Rollins, "is to know exactly what the customer wants when they want it, so they will possess no waste."

    The company has been propelled by this thinking ever since Michael Dell started selling PCs from his college dorm elbowroom in 1983. The Web makes the process virtually seamless, by allowing the company to easily collect customized, digitized data that are ready for delivery to the people who necessity them. The result is an entire organization driven by orders placed by individual customers, an organization that does more Web-based commerce than almost anyone else. Dell's future doesn't depend on faster chips or modems--it depends on greater mastery of mass customization, of streamlining the stream of quality information.

    It's not much of a surprise that a leading tech company relish Dell is using software and the Net in such innovative ways. What's startling is the extent to which companies in other industries are embracing mass customization. engage Mattel. Starting by October, girls will subsist able to log on to barbie.com and design their own friend of Barbie's. They will subsist able to elect the doll's skin tone, eye color, hairdo, hair color, clothes, accessories, and appellation (6,000 permutations will subsist available initially). The girls will even fill out a questionnaire that asks about the doll's likes and dislikes. When the Barbie pal arrives in the mail, the girls will find their doll's appellation on the package, along with a computer-generated paragraph about her personality.

    Offering such a product without the Net would subsist next to impossible. Mattel does bear specific versions of Barbie for customers such as Toys "R" Us, and the company customizes cheerleader Barbies for universities. But this will subsist the first time Mattel produces Barbie dolls in lots of one. relish Dell, Mattel must spend high-end manufacturing and logistics software to ensure that the order data on its Website are distributed to the parts of the company that necessity them. The only actual concern is whether Mattel's systems can ply the expected claim in a timely fashion. prerogative now, marketing VP Anne Parducci is shooting for delivery of the dolls within six weeks--a bit much considering that that is how long it takes to gain a custom-ordered BMW.

    Nevertheless, Parducci is pumped. "Personalization is a dream they possess had for several years," she says. Parducci thinks the custom Barbies could become one of next year's hottest toys. Then, says Parducci, "we are going to build a database of children's names, to develop a one-to-one relationship with these girls." That may sound creepy, but fraction of mass customization is treating your customers, even preteens, as adults. By allowing the girls to define beauty in their own terms, Mattel is in theory helping them feel trustworthy about themselves even as it collects personal data. That's quite a step for a company that has stamped out its own stereotypes of beauty for decades, but Parducci's market testing shows that girls' enthusiasm for being a mode designer or creating a personality is "through the roof."

    Levi Strauss besides likes giving customers the desultory to play mode designer. For the past four years it has made measure-to-fit women's jeans under the Personal Pair banner. In October, Levi's will relaunch an expanded version called Original Spin, which will offer more options and will feature men's jeans as well.

    With the assist of a sales associate, customers will create the jeans they want by picking from six colors, three basic models, five different leg openings, and two types of fly. Then their waist, butt, and inseam will subsist measured. They will try on a unpretentious pair of test-drive jeans to bear sure they relish the fitting before the order is punched into a Web-based terminal linked to the stitching machines in the factory. Customers can even give the jeans a name--say, Rebel, for a pair of black ones. Two to three weeks later the jeans arrive in the mail; a bar-code tag sealed to the pocket lining stores the measurements for simple reordering.

    Today a fully stocked Levi's store carries approximately 130 ready-to-wear pairs of jeans for any given waist and inseam. With Personal Pair, that number jumped to 430 choices. And with Original Spin, it will leap again, to about 750. Sanjay Choudhuri, Levi's director of mass customization, isn't in a race to add more choices. "It is critical to carefully pick the choices that you offer," says Choudhuri. "An unlimited amount will create inefficiencies at the plant." Dell Computer's Rollins agrees: "We want to offer fewer components totality the time." To these two, mass customization isn't about sempiternal choices but about offering a well number of standard parts that can subsist mixed and matched in thousands of ways. That gives customers the illusion of boundless election while keeping the complexity of the manufacturing process manageable.

    Levi's charges a slight premium for custom jeans, but what Choudhuri really likes about the process is that Levi's can become your "jeans adviser." Selling off-the-shelf jeans ends a relationship; the customer walks out of the store as anonymous as anyone else on the street. Customizing jeans starts a relationship; the customer likes the fit, is ready for reorders, and forks over his appellation and address in case Levi's wants to ship him promotional offers. And customers who design their own jeans bear the consummate focus group; Levi's can apply what it learns from them to the jeans it mass-produces for the repose of us.

    If Levi's experiment pays off, other apparel makers will ensue its lead. In the not-so-distant future people may simply walk into body-scanning booths where they will subsist bathed with patterns of white light that will determine their exact three-dimensional structure. A not-for-profit company called [TC]2, funded by a consortium of companies including Levi's, is developing just such a technology. final year some MIT industry students proposed a similar credence for a custom-made bra company dubbed consummate Underwear.

    Morpheus Technologies, a wacky startup in Portland, Me., hopes to set up studios equipped with carcass scanners. Founder Parker Poole III wants to "digitize people and connect their measurement data to their credit cards." Someone with the foresight to subsist scanned by Morpheus could then call up Eddie Bauer, say, give his credit card number, and order a robe that matches his dimensions. His digital self could besides subsist sent to Brooks Brothers for a suit. Gone will subsist the days of attentive men kneeling on the floor with pins in their mouths. Progress does possess its price.

    Thirty years ago auto manufacturers were, effectively, mass customizers. People would spend hours in the office of a car dealer, picking through pages of options. But that ended when car companies tried to help manufacturing efficiency by offering shrimp more than a few standard options packages. BMW wants to turn back the clock. About 60% of the cars it sells in Europe are built to order, vs. just 15% in the U.S. Europeans appear willing to wait three to four months for a vehicle, while most Americans won't wait longer than four weeks.

    Now the company wants to bear better spend of its customer database to gain more Americans to custom-order. BMW dealers deliver about $450 in inventory costs on every such order. Reinhard Fischer, head of logistics for BMW of North America, says, "The gargantuan battle is to engage cost out of the distribution chain. The best way to attain that is to build in just the things a consumer wants."

    Since most BMWs in the U.S. are leased, the company knows when customers will necessity a original car. Some dealers now call customers a few months before their leases are up to descry whether they'd relish to custom-order their next car. Soon, however, customers will subsist able to configure their own car online and ship that info to a dealer. Fischer can even descry a day when the Website will offer data about vehicles sailing on ships from Germany, so that people can descry whether a car matching their preferences is already on the way. That does, of course, raise the question, Why not ship the requests directly to BMW, circumventing dealers altogether? Says Fischer: "We don't want to purge their role, but maybe they should possess a 7% margin, not 16%." Ouch.

    Such dilemmas are inevitable, given that mass customization streamlines the order process. What's more, mass customization is about creating products--be they PCs, jeans, cars, eyeglasses, loans, or even industrial soap--that match your needs better than anything a traditional middleman can possibly order for you.

    LensCrafters, for instance, has made quick, in-store production of customized lenses common. But Tokyo-based Paris Miki takes the process a step further. Using special software, it designs lenses and a frame that conform both to the shape of a customer's face and to whether he wants, say, casual frames, a sports pair, sunglasses, or more formal specs. The customer can check out on a monitor various choices superimposed over a scanned image of his face. Once he chooses the pair he likes, the lenses are ground and the rimless frames attached.

    While they tend to reflect of automation as a process that eliminates the necessity for human interaction, mass customization makes the relationship with customers more critical than ever. ChemStation in Dayton has about 1,700 industrial-soap formulas--for car washes, factories, landfills, railroads, airlines, and mines. The company analyzes items that are to subsist cleaned (recent ones in its labs involve flutes and goose down) or visits its customers' premises to dissect their dirt. After the analysis, the company brews up a special batch of cleanser. The soap is then placed on the customer's property in reusable containers ChemStation monitors and keeps full. For most customers, teaching another company their cleansing needs is not worth the effort. About 95% of ChemStation's clients never leave.

    Hotels that want you to retain coming back are using software to personalize your experience. totality Ritz-Carlton hotels, for instance, are linked to a database filled with the quirks and preferences of half-a-million guests. Any bellhop or desk clerk can find out whether you are allergic to feathers, what your favorite newspaper is, or how many extra towels you like.

    Wells Fargo, the largest provider of Internet banking, already allows customers to apply for a home-equity loan over the Net and gain a three-second conclusion on a loan structured specifically for them. A lot of behind-the-scenes technology makes this possible, including real-time links to credit bureaus, databases with checking-account histories and property values, and software that can attain cash-flow analysis. With a few pieces of customized information from the loan seeker, the software whips into action to bear a quick decision.

    The bank besides uses similar software in its small-business lending unit. According to vice chairman Terri Dial, Wells Fargo used to turn away lots of qualified small businesses--the loans were too small for Wells to justify the time spent on credit analysis. But now the company can collect a few key details from applicants, customize a loan, and approve or traverse credit in four hours--down from the four days the process used to take. In some categories that Wells once virtually ignored, loan approvals are up as much as 50%. Says Dial: "You either invest in the technology or gain out of that line of business."

    She'd better retain investing. Combine the software that enables customization with the ubiquity of the Web, and you gain a situation that threatens Wells' very existence. If consumers grow accustomed to designing their own products, will they trust brand-name manufacturers and service providers or will they turn to a original benevolent of middleman? candid Shlier, a director of research at the Gartner Group in Stamford, Conn., sees disintermediaries emerging totality over the Net to assist people sift through the thousands of choices presented to them. In fiscal services, he suggests, there is "a original role for a trusted adviser, maybe someone who doesn't own any banks."

    Shlier's middleman sounds a lot relish Intuit, which lets visitors to its quicken.com Website apply for and purchase mortgages from a variety of lenders, fill out their taxes, or set up a portfolio to track their stocks, bonds, and mutual funds. Tapan Bhat, the exec who oversees quicken.com, says, "The Web is probably the medium most attuned to customization, yet so many sites are centered on the company instead of on the individual." What would decoy someone to Levi's if she could instead visit a clothing Website that stored her digital dimensions and ordered custom-fit jeans from the manufacturer with the best cost and fit? Elaborates Pehong Chen, CEO of Internet software outfit BroadVision: "The Nirvana is that you are so proximate to your customers, you can answer totality their needs. Even if you don't bear the detail yourself, you own the relationship."

    Amazon.com has three million relationships. It sells books online and now is touching into music (with videos probably next). Every time someone buys a book on its Website, Amazon.com learns her tastes and suggests other titles she might enjoy. The more Amazon.com learns, the better it serves its customers; the better it serves its customers, the more loyal they become. About 60% are iterate buyers.

    The Web is a supermall of mass customizers. You can drop music tracks on your own CDs (cductive.com); elect from over a billion options of printed art, mats, and frames (artuframe.com); gain stock picks geared to your goals (personalwealth.com); or bear your own vitamins (acumins.com). And you can gain totality kinds of tailored data; NewsEdge, for example, will ship a customized newspaper to your PC.

    These companies want to retain customers tickled by giving them a product that cannot subsist compared to a competitor's. Acumin, for instance, blends vitamins, herbs, and minerals per customers' instructions, compressing up to 95 ingredients into three to five pills. If a customer wants to start taking a original supplement, totality Acumin needs to attain is add it to the blend.

    Acumin's products address what Pine calls customer sacrifice--the compromise they totality bear when they can't gain exactly the product they want. CEO Brad Oberwager started the company two years ago, when his sister, who was undergoing a special cancer radiation treatment, couldn't find a multivitamin without iodine. (Her doctor had told her to avoid iodine.) "If someone would create a vitamin just for me, I would buy it," she told her brother. So he did.

    The Web will bear that benevolent of response the norm. Sure, there are any number of ways for consumers to provide a company with information about their preferences--they can call, they can write, or, heck, they can even walk into the brick-and-mortar store. But the Web changes everything--the information arrives in a digitized configuration ready for broadcast. Says i2 CEO Sanjiv Sidhu, "The Internet is bringing society into a culture of speed that has not really existed before." As original middlemen customize orders for the masses, differentiating one company from its competitors will become tougher than ever. Responding to cost cuts or quality improvements will continue to subsist important, but the key differentiator may subsist how quickly a company can serve a customer. Says Artuframe.com CEO Bill Lederer: "Mass customization is novel today. It will subsist common tomorrow." If he is right, the Web will wind up creating a offbeat competitive landscape, where companies temporarily connect to answer one customer's desires, then disband, then reconnect with other enterprises to answer a different order from a different customer.

    That's the vision anyway. For now, companies are struggling to engage the first steps toward mass customization. The ones that are already there possess been working on the process for years. Matthew Sigman is an executive at R.R. Donnelley & Sons, whose digital publishing industry prints textbooks customized by individual college professors. "The challenge," Sigman warns, "is that if you are making units of one, your margin for error is zero." Custom-fit jeans attain forward with a money-back guarantee. Levi's can't afford for you not to relish them.



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